A Method of Driver’s Eyes Closure and Yawning Detection for Drowsiness Analysis by Infrared Camera

Tipprasert, Wisaroot; Charoenpong, Theekapun; Chianrabutra, Chamaporn; Sukjamsri, Chamaiporn

doi:10.1109/ica-symp.2019.8646001

Cited by 37 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [10] Viola Jones is used for eye detection and yawning detection which is followed by a binary support vector machine and correlation coefficient matching performs the tracking of eyes and yawning detection. In [11] an infrared camera is used to improve detection rates along with Haar-based classifiers and HoG to convert the support vector machine images to vector data. In [12] OpenFace and a 3D CNN are used for facial feature detection.…”

Section: B Visual Features-based Methodsmentioning

confidence: 99%

Drowsiness Detection Based on Eye Closure and Yawning Detection

Rani*,

Mohana

2019

IJRTE

View full text Add to dashboard Cite

The number of major road accidents that occur per day is on a rise and most of them are attributed to being the driver’s fault. According to the survey done in 2015, drivers are held responsible for approximately 78% of the accidents. To minimize the occurrence of these incidents a monitoring system that alerts the driver when he succumbs to sleep is proposed. This algorithm processes live video feed focused on the driver’s face and tracks his eye and mouth movements to detect eye closure and yawning rates. An alarm sounds if the driver is drowsy or already asleep. Haar-cascade classifiers run parallelly on the extracted facial features to detect eye closure and yawning.

show abstract

Section: B Visual Features-based Methodsmentioning

confidence: 99%

Drowsiness Detection Based on Eye Closure and Yawning Detection

Rani*,

Mohana

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…An HMI with an emotional voice alert system adapted to the driver's emotions by using a CNN (Convolutionnal Neural Network) is used to detect the driver's emotions [18]. Tipprasert et al [19] suggests using infrared camera with Haar cascade for eyes closer and yawning detection in low light condition. Baek et al [20] employs an integrated camera on the dashboard with MCT (modified census transform features) on ada boost classifier with LBF (Local Binary Features) mapping and global linear regression with random forest to detect the landmarks of the face than PerClos (PERcentage of CLOsure) is applied for the eyes.…”

Section: Related Workmentioning

confidence: 99%

Driver Drowsiness Detection and Tracking Based on Yolo with Haar Cascades and ERNN

Amira¹,

Zoulikha²,

Hector³

2021

IJSSE

View full text Add to dashboard Cite

When it comes to dangerous drowsiness, the security of the driver and peoples surrounding him depends only on his decisions. This paper expose both of driver drowsiness detector and driving behaviour corrector method based on a conversational assistant agent able to discern and try to avoid driver sleepiness on the wheel, by using a camera to get face's images of the driver in real time, and an agent displayed in the screen and monitors the driver's face in order to warn of drowsiness and to avoid a possible accident. For that, we used Haar cascade with a simplified Yolo-Lite merged with a tree word for detection, followed by the proposed PerStat method with MLP instead of PerClos method which gave a difference of (20%). For the recognition, and helped to raise the problem of the vanishing gradient being used as sequential pre-processing for an ERNN which will generate the agent feedbacks.

show abstract

“…The drowsy state of a person can be easily identified using two parameters which are eye-blink frequency and yawn count. W. Tipprasert, et al [1] discusses about method of driver eyes closure and yawning detection for drowsiness using Infrared camera. The eye detection process is examined using the signal processing technique.…”

Section: Introductionmentioning

confidence: 99%

Drowsiness detection using Eye-Blink frequency and Yawn count for Driver Alert

2019

IJITEE

View full text Add to dashboard Cite

Several reasons can be sighted for the cause of these road accidents. Few of them include lack of sleep, drunk driving, violation of traffic rules, etc. Amongst them, the state of drowsiness and drunk driving alone contributes to 36% of accidents. Though a number of national schemes and traffic rules have been implemented to avoid these road accidents, it could only bring down the accident rate by 10%. As car accidents are one of the major issues of concern, this paper will be discussing mainly on Drunk driving or drowsiness. In these recent years, various methods have been proposed to implement drowsiness detection based on Hough transforms. Here, in this paper, we have determined a technique to detect drowsiness among car drivers and alert them whenever they tend to sleep. The algorithm is based on eye-blink and yawn frequency. It deals with an eye blink yawn frequency algorithm that uses eye coordinates to keep track of person and determine the open or closed state of the eye and generate an alarm if the driver is drowsy. The yawn count is determined by checking the frequency of yawn count with a minimum threshold value.

show abstract

A Method of Driver’s Eyes Closure and Yawning Detection for Drowsiness Analysis by Infrared Camera

Cited by 37 publications

References 8 publications

Drowsiness Detection Based on Eye Closure and Yawning Detection

Drowsiness Detection Based on Eye Closure and Yawning Detection

Driver Drowsiness Detection and Tracking Based on Yolo with Haar Cascades and ERNN

Drowsiness detection using Eye-Blink frequency and Yawn count for Driver Alert

Contact Info

Product

Resources

About